Characterizing resonant species using nuclear magnetic resonance (NMR) can include identifying different properties of a resonant species (e.g., T1 spin-lattice relaxation, T2 spin-spin relaxation, proton density). Other properties of resonant species like tissue types and super-position of attributes can also be identified using NMR signals. NMR signals can be analyzed to determine an apparent diffusion coefficient from which a diffusion map may be produced. These properties and others may be identified simultaneously using magnetic resonance fingerprinting (MRF), which is described in Magnetic Resonance Fingerprinting, Ma D et al., Nature 2013:495, (7440):187-192 and in U.S. Pat. No. 8,723,518, which is incorporated herein by reference.
Conventional magnetic resonance (MR) pulse sequences include repetitive similar preparation phases, waiting phases, and acquisition phases that serially produce signals from which images can be made. The preparation phase determines when a signal can be acquired and determines the properties of the acquired signal. For example, a first pulse sequence may produce a T1-weighted signal at a first echo time (TE) while a second pulse sequence may produce a T2-weighted signal at a second TE. These conventional pulse sequences typically provide qualitative results where data are acquired with various weightings or contrasts that highlight a particular parameter (e.g., T1 relaxation, T2 relaxation).
When MR images are generated, they may be viewed by a radiologist or surgeon who interprets the qualitative images for specific disease signatures. The radiologist may examine multiple image types (e.g., T1-weighted, T2-weighted) acquired in multiple imaging planes to make a diagnosis. The radiologist or other individual examining the qualitative images may need particular skill to be able to assess changes from session to session, from machine to machine, and from machine configuration to machine configuration.
Unlike conventional magnetic resonance imaging (MRI), MRF employs a series of varied sequence blocks that simultaneously produce different signal evolutions in different resonant species (e.g., tissues) to which the RF is applied. The term “resonant species”, as used herein, refers to an item (e.g., water, fat, tissue, material) that can be made to resonate using NMR. By way of illustration, when RF energy is applied to a volume that has both normal tissue and abnormal (e.g., cancerous) tissue, then both the normal tissue and abnormal tissue will produce an NMR signal. However the “normal tissue signal” and the “abnormal tissue signal” may be different and distinguishable using MRF. The different signals can be collected over a period of time to identify a signal evolution for the volume. Resonant species in the volume can then be characterized by comparing the signal evolution to known evolutions. Characterizing the resonant species may include identifying a material or tissue type, or may include identifying MR parameters associated with the resonant species. The “known” evolutions may be, for example, simulated evolutions or previously acquired evolutions. A large set of known evolutions may be stored in a comprehensive dictionary.
The data acquired with MRF techniques are compared with the dictionary of signal evolutions that have been generated for different acquisition parameters from magnetic resonance signal models, such as Bloch equation-based physics simulations or that have been collected from previous acquisitions. This comparison allows estimation of the MR parameters.
In order to limit the number of comparisons that need to be made, a dictionary of likely combinations is often created a priori. It is often necessary, however, for this dictionary to have between a hundred thousand to over a million dictionary elements in order to achieve a clinically acceptable resolution for the estimated tissue properties. Matching received signals to the dictionary is a computationally demanding problem. For instance, the process includes comparing a time course of signals from the subject, each having thousands of voxels, to upwards of one million dictionary elements, each having a thousand or more time points. As an example, using conventional approaches and hardware, it may take over three minutes to match the T1, T2, and B0 values for a 128×128 image matrix based on a dictionary having 200,000 elements that each include 1,000 time points. In other situations, matching may take five minutes or even longer, depending on the dictionary size, the number of time points, or the size of the image.
Compared to conventional MR methods, in which signals may have a real part upon which a pattern matching decision can be made, the MRF signal evolutions used by example methods and apparatus include complex values with an arbitrary phase relationship. These complex values with arbitrary phase relationships may result in correlated signal evolutions, and challenge the effectiveness of conventional matching approaches in a clinical environment.